Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps in the diagnosis, prediction, and prevention of COVID-19 and post-COVID-19 patients. Nowadays, the biomedical and clinical domains widely used the electromyography (EMG) signal due to its ability to differentiate various neuromuscular diseases. In general, nerves or muscles and the spinal cord influence numerous neuromuscular disorders. The clinical examination plays a major role in early finding and diagnosis of these diseases; this research study focused on the prediction of muscular paralysis using EMG signals. Machine learning–based diagnosis of the diseases has been widely used due to its efficiency and the hybrid feature extraction (FE) methods with deep learning classifier are used for the muscular paralysis disease prediction. The discrete wavelet transform (DWT) method is applied to decompose the EMG signal and reduce feature degradation. The proposed hybrid FE method consists of Yule-Walker, Burg’s method, Renyi entropy, mean absolute value, min-max voltage FE, and other 17 conventional features for prediction of muscular paralysis disease. The hybrid FE method has the advantage of extract the relevant features from the signals and the Relief-F feature selection (FS) method is applied to select the optimal relevant feature for the deep learning classifier. The University of California, Irvine (UCI), EMG-Lower Limb Dataset is used to determine the performance of the proposed classifier. The evaluation shows that the proposed hybrid FE method achieved 88% of precision, while the existing neural network (NN) achieved 65% of precision and the support vector machine (SVM) achieved 35% of precision on whole EMG signal.

许多 2019 年冠状病毒病 (COVID-19) 和 COVID-19 后患者都会出现肌肉疲劳。早期检测肌肉疲劳和肌肉麻痹有助于诊断、预测和预防 COVID-19 和 COVID-19 后患者。如今，生物医学和临床领域广泛使用肌电图（EMG）信号，因为它能够区分各种神经肌肉疾病。一般来说，神经或肌肉和脊髓影响许多神经肌肉疾病。临床检查对于这些疾病的早期发现和诊断起着重要作用；这项研究的重点是使用肌电图信号预测肌肉麻痹。基于机器学习的疾病诊断因其效率而被广泛应用，并且具有深度学习分类器的混合特征提取（FE）方法被用于肌肉麻痹疾病预测。应用离散小波变换（DWT）方法来分解肌电信号并减少特征退化。所提出的混合有限元方法由 Yule-Walker、Burg 方法、Renyi 熵、平均绝对值、最小-最大电压有限元以及其他 17 个用于预测肌肉麻痹疾病的常规特征组成。混合有限元方法的优点是从信号中提取相关特征，并应用Relief-F特征选择（FS）方法为深度学习分类器选择最佳相关特征。加州大学欧文分校 (UCI) 的肌电图下肢数据集用于确定所提出的分类器的性能。评估表明，所提出的混合有限元方法在整个肌电信号上实现了 88% 的精度，而现有的神经网络 (NN) 实现了 65% 的精度，支持向量机 (SVM) 实现了 35% 的精度。